{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1f8b7060",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from collections import Counter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ed0f044c",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"seq.data\",\"r\",encoding=\"utf-8\") as f:\n",
    "    lines = f.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bc2c293e",
   "metadata": {},
   "outputs": [],
   "source": [
    "cntq = {}\n",
    "cnta={}\n",
    "for line in lines:\n",
    "    qa = line.strip().split(\"\\t\")\n",
    "    if len(qa) == 2:\n",
    "        q= qa[0]\n",
    "        a=qa[1]\n",
    "        lenq = len(q.split(\" \"))\n",
    "        lena = len(a.split(\" \"))\n",
    "        cntq[lenq] = cntq.get(lenq,0)+1\n",
    "        cnta[lena] = cnta.get(lena,0)+1\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4173c73c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b1e18fa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2800x800 with 0 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2800x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(28,8),dpi=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f8103a4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 20 artists>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1 = list(cnta.keys())\n",
    "y1 = list(cnta.values())\n",
    "\n",
    "a = list(zip(x1,y1))\n",
    "\n",
    "l = sorted(a,key=lambda x:x[0])\n",
    "l = l[:20]\n",
    "x = [i[0] for i in l]\n",
    "y = [i[1] for i in l]\n",
    "\n",
    "plt.bar(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7e638f19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 62869),\n",
       " (2, 69644),\n",
       " (3, 86611),\n",
       " (4, 77358),\n",
       " (5, 54340),\n",
       " (6, 33872),\n",
       " (7, 21933),\n",
       " (8, 13639),\n",
       " (9, 9325),\n",
       " (10, 6274),\n",
       " (11, 4440),\n",
       " (12, 3063),\n",
       " (13, 2114),\n",
       " (14, 1552),\n",
       " (15, 1126),\n",
       " (16, 831),\n",
       " (17, 622),\n",
       " (18, 440),\n",
       " (19, 333),\n",
       " (20, 222),\n",
       " (21, 181),\n",
       " (22, 159),\n",
       " (23, 138),\n",
       " (24, 113),\n",
       " (25, 78),\n",
       " (26, 74),\n",
       " (27, 46),\n",
       " (28, 38),\n",
       " (29, 33),\n",
       " (30, 25),\n",
       " (31, 32),\n",
       " (32, 29),\n",
       " (33, 14),\n",
       " (34, 16),\n",
       " (35, 21),\n",
       " (36, 8),\n",
       " (37, 15),\n",
       " (38, 14),\n",
       " (39, 14),\n",
       " (40, 18),\n",
       " (41, 11),\n",
       " (42, 11),\n",
       " (43, 6),\n",
       " (44, 6),\n",
       " (45, 7),\n",
       " (46, 3),\n",
       " (47, 7),\n",
       " (48, 8),\n",
       " (49, 1),\n",
       " (50, 3)]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d45c87be",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'dict_keys' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-a543b1a22667>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcntq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcntq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'dict_keys' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "plt.plot(cntq.keys(),cntq.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ada3dce0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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